With the improved NAS and LACS, we search for high accuracy CNNs for efficient inference on TPUv3 [1]}, [2]} and GPUv100 [3]}. Our search results in a new model family named EfficientNet-X (with differences on TPU and GPU) that achieve a better accuracy and latency trade-offs than the state-of-the-art. EfficientNet-X models are up to more than 2X faster on TPUv3 and GPUv100 respectively than EfficientNet [4]} with comparable accuracy. Moreover, EfficientNet-X models achieve 30% more speedup compared to EfficientNet when moving from TPUv2 to TPUv3, demonstrating the generality of our search method across different accelerator generations. EfficientNet-X is also faster than other SOTA models, with on average (geo-mean) 82% and 48% faster than RegNet and ResNeSt respectively on GPUv100 and 7X and 48% faster than RegNet and ResNeSt respectively on TPUv3.
